Predictive process control for a manufacturing process

ABSTRACT

Aspects of the disclosed technology encompass the use of a deep learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving a plurality of control values from two or more stations, at a deep learning controller, wherein the control values are generated at the two or more stations deployed in a manufacturing process, predicting an expected value for an intermediate or final output of an article of manufacture, based on the control values, and determining if the predicted expected value for the article of manufacture is in-specification. In some aspects, the process can further include steps for generating control inputs if the predicted expected value for the article of manufacture is not in-specification. Systems and computer-readable media are also provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/519,102, filed Jul. 23, 2019, which claims the priority to U.S.Application Ser. No. 62/865,859, filed Jun. 24, 2019, which areincorporated by reference in their entireties.

BACKGROUND 1. Technical Field

The present disclosure generally relates to systems, apparatus andmethods for predictive process control (PPC) of a manufacturing process.More particularly, the subject technology provides improvements to themanufacturing process and in particular, encompasses systems and methodsfor adaptively controlling various stations in the manufacturingprocess, as well as optimizing the final manufacturing product andprocess, based on predictions made using machine-learning models. Asdiscussed in further detail below, some aspects of the technologyencompass systems and methods for training the machine-learning models.

2. Introduction

To manufacture products that consistently meet desired designspecifications, safely, timely and with minimum waste, requires constantmonitoring and adjustments to the manufacturing process.

SUMMARY OF THE INVENTION

In some aspects, the disclosed technology relates to the use of a deeplearning controller for monitoring and improving a manufacturingprocess. In some embodiments, the disclosed technology encompasses acomputer-implemented method that includes steps for receiving aplurality of control values from two or more stations, at a deeplearning controller, wherein the control values are generated at the twoor more stations deployed in a manufacturing process, predicting, by thedeep learning controller, an intermediate or final output of an articleof manufacture, based on the control values, determining, by the deeplearning controller, if the predicted intermediate or final outputspecification for the article of manufacture is in-specification, andgenerating, by the deep learning controller, one or more control inputsif the predicted intermediate or final output for the article ofmanufacture is not in-specification, wherein the one or more controlinputs are configured to cause an intermediate or final output for thearticle of manufacture to be in-specification.

In another embodiment, the disclosed technology encompasses a systemhaving one or more processors, and a non-transitory memory storinginstructions that, when executed by the one or more processors, causethe one or more processors to perform operations including: receiving aplurality of control values from two or more stations, at a deeplearning controller, wherein the control values are generated at the twoor more stations deployed in a manufacturing process, predicting, by thedeep learning controller, an intermediate or final output of an articleof manufacture, based on the control values, determining, by the deeplearning controller, if the predicted intermediate or final output forthe article of manufacture is in-specification, and generating, by thedeep learning controller, one or more control inputs if the predictedintermediate or final output for the article of manufacture is notin-specification, wherein the one or more control inputs are configuredto cause an intermediate or final output for the article of manufactureto be in-specification.

In yet another embodiment, the disclosed technology encompasses anon-transitory computer-readable storage medium comprising instructionsstored therein, which when executed by one or more processors, cause theone or more processors to perform operations including: receiving aplurality of control values from two or more stations, at a deeplearning controller, wherein the control values are generated at the twoor more stations deployed in a manufacturing process, predicting, by thedeep learning controller, an intermediate or final output of an articleof manufacture, based on the control values, determining, by the deeplearning controller, if the predicted intermediate or final output forthe article of manufacture is in-specification, and generating, by thedeep learning controller, one or more control inputs if the predictedintermediate or final output for the article of manufacture is notin-specification, wherein the one or more control inputs are configuredto cause an intermediate or final output for the article of manufactureto be in-specification.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting in their scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIG. 1 illustrates an example process for controlling a station usingStatistical Process Control (SPC).

FIG. 2 shows an example embodiment of a deep learning controllerconfigured for predictive process control (PPC).

FIG. 3 shows an example method for training a deep learning controller.

FIG. 4 shows an example method for implementing predictive processcontrol.

FIG. 5 shows an example method for creating a robust data training setusing predictive process control.

FIG. 6 shows an example method for optimizing a manufacturing processusing predictive process control.

FIG. 7 shows a practical application of optimizing a manufacturingprocess using predictive process control.

FIG. 8 shows an example method for logging and creating data outputs.

FIG. 9 shows the general configuration of an embodiment of a deeplearning controller that can implement predictive process control.

DETAILED DESCRIPTION

The detailed description set forth below is intended as a description ofvarious configurations of the subject technology and is not intended torepresent the only configurations in which the subject technology can bepracticed. The appended drawings are incorporated herein and constitutea part of the detailed description. The detailed description includesspecific details for the purpose of providing a more thoroughunderstanding of the subject technology. However, it will be clear andapparent that the subject technology is not limited to the specificdetails set forth herein and may be practiced without these details. Insome instances, structures and components are shown in block diagramform in order to avoid obscuring the concepts of the subject technology.

Additional details relating to the use of image classification inmanufacturing control are provided by U.S. Provisional Application No.62/836,202, entitled “DEPLOYMENT OF AN IMAGE CLASSIFICATION MODEL ON ASINGLE-BOARD COMPUTER FOR MANUFACTURING CONTROL,” which is herebyincorporated by reference herein in its entirety.

Additional details relating to the user of computation models foroptimizing assembly/manufacturing operations are provided by U.S.Provisional Patent Application No. 62/836,192, entitled “A COMPUTATIONMODEL FOR DECISION-MAKING AND ASSEMBLY OPTIMIZATION IN MANUFACTURING,”and U.S. patent application Ser. No. 16/289,422, entitled “DYNAMICTRAINING FOR ASSEMBLY LINES,” both of which are hereby incorporated byreference in their entirety.

The manufacturing process is complex and comprises raw materials beingprocessed by different process stations (or “stations”) until a finalproduct (referred to herein as “final output”) is produced. With theexception of the final process station, each process station receives aninput for processing and outputs an intermediate output that is passedalong to a subsequent (downstream) process station for additionalprocessing. The final process station receives an input for processingand outputs the final output.

Each process station can include one or more tools/equipment thatperforms a set of process steps on: received raw materials (this canapply to a first station or any of the subsequent stations in themanufacturing process) and/or the received output from a prior station(this applies to any of the subsequent stations in the manufacturingprocess). Examples of process stations can include, but are not limitedto conveyor belts, injection molding presses, cutting machines, diestamping machines, extruders, CNC mills, grinders, assembly stations, 3Dprinters, quality control and validation stations. Example process stepscan include: transporting outputs from one location to another (asperformed by a conveyor belt); feeding material into an extruder,melting the material and injecting the material through a mold cavitywhere it cools and hardens to the configuration of the cavity (asperformed by an injection molding presses); cutting material into aspecific shape or length (as performed by a cutting machine); pressingmaterial into a particular shape (as performed by a die stampingmachine).

In some manufacturing processes, several process stations can run inparallel. In other words, a single process station can send itsintermediate output to one or more stations (e.g., 1 to N stations), anda single process station can receive and combine intermediate outputsfrom one to n stations. Moreover, a single process station can performthe same process step or different process steps, either sequentially ornon-sequentially, on the received raw material or intermediate outputduring a single iteration of a manufacturing process.

Operation of each process station can be governed by one or more processcontrollers. In some implementation, each process station has one ormore process controllers (referred to herein as “a station controller”)that are programmed to control the operation of the process station (theprogramming algorithms referred to herein as “control algorithms”).However, in some aspects, a single process controller may be configuredto control the operations of two or more process stations.

An operator, or control algorithms, can provide the station controllerwith station controller setpoints (or “setpoints” or “controllersetpoints” or CSPs) that represent the desired value/or range of valuesfor each control value. The attributes/parameters associated with astation's tools/equipment/process steps that can be measured during theoperation of the station are either control values or station values. Ifthe measured attributes/parameters are also used to control the station,then they are “control values.” Otherwise, the measuredattributes/parameters are “station values.” Examples of control orstation values include, but are not limited to: speed, temperature,pressure, vacuum, rotation, current, voltage, power, viscosity,materials/resources used at the station, throughput rate, outage time,noxious fumes, the type of steps and order of the steps performed at thestation. Although, the examples are the same, whether a measuredattribute/parameter is considered a control value or a station value,will depend on the particular station and whether the measuredattribute/parameter is used to control the station or is simply abyproduct of the operation of the station.

The control algorithms can also include instructions for monitoringcontrol values, comparing control values to corresponding setpoints anddetermining what actions to take when the control value is not equal to(or not within a defined range of) a corresponding station controllersetpoint. For example, if the measured present value of the temperaturefor the station is below the setpoint, then a signal may be sent by thestation controller to increase the temperature of the heat source forthe station until the present value temperature for the station equalsthe setpoint. Conventional process controllers used in the manufacturingprocess to control a station are limited, because they follow staticalgorithms (e.g., on/off control, PI control, PID control, Lead/Lagcontrol) for adjusting setpoints and prescribing what actions to takewhen a control value deviates from a setpoint. Conventional processcontrollers also have limited capability, if any, to analyze non-controlvalues such as ambient conditions (e.g., external temperature, humidity,light exposure, wear and tear of the station), station values,intermediate or final output values, feedback from other processstations, and to make dynamic adjustments to a station controller'ssetpoints or control algorithms, that control the operation of anassociated station.

A process value, as used herein refers to a station value or controlvalue that is aggregated or averaged across an entire series of stations(or a subset of the stations) that are part of the manufacturingprocess. Process values can include, for example, total throughput time,total resources used, average temperature, average speed.

In addition to station and process values, various characteristics of aprocess station's product output (i.e., intermediate output or finaloutput) can be measured, for example: temperature, weight, productdimensions, mechanical, chemical, optical and/or electrical properties,number of design defects, the presence or absence of a defect type. Thevarious characteristics that can be measured, will be referred togenerally as “intermediate output value” or “final output value.” Theintermediate/final output value can reflect a single measuredcharacteristic of an intermediate/final output or an overall score basedon a specified set of characteristics associated with theintermediate/final output that are measured and weighted according to apredefined formula.

Mechanical properties can include hardness, compression, tack, densityand weight. Optical properties can include absorption, reflection,transmission, and refraction. Electrical properties can includeelectrical resistivity and conductivity. Chemical properties can includeenthalpy of formation, toxicity, chemical stability in a givenenvironment, flammability (the ability to burn), preferred oxidationstates, pH (acidity/alkalinity), chemical composition, boiling point,vapor point). The disclosed mechanical, optical, chemical and electricalproperties are just examples and are not intended to be limiting.

As shown in FIG. 1 , the value for the intermediate output of a processstation, as well as the value for the final output produced by themanufacturing process, can be evaluated according to Statistical ProcessControl (SPC). Using statistics, SPC tracks, on a station by stationbasis: (1) the values of the intermediate outputs or final outputsgenerated by a station over a period of time; and (2) the mean of theintermediate or final output values and the standard deviation from themean. For example, FIG. 1 shows intermediate output values for aparticular station. Each dot represents an intermediate output producedby the station and its value, and the distance of the dot from the mean(represented by the black line in the middle) shows how much theintermediate output value deviates from the mean for that particularstation. An upper and lower control limit (UCL AND LCL) can be definedfor the particular station (for example one or more standard deviationsabove or below the mean). FIG. 1 shows the upper and lower controllimits are set at three standard deviations above and below the mean(represented by the dashed lines). The upper and lower control limitsare usually narrower than the upper and lower specification limits (USLAND LSL) defined for the intermediate/final output values.

In some aspects, statistical values for intermediate outputs and/orfinal outputs can be used to make determinations as to when an articleof manufacture is “in-specification,” i.e., when an output has achievedcertain pre-specified design requirements. As such, in-specification canrefer to an article of manufacture, or a characteristic of an article ofmanufacture, that meets or exceeds a specified design requirement or setof requirements. By way of example, an article of manufacture that isdeemed to be in-specification may be one that achieves specifiedstatistical requirements, such as having acceptable deviations from anideal or average (mean) value.

As long as the values for the intermediate/final output are within theupper and lower control limits, then the process station/overall processis considered to be in control and typically, no interventions orcorrective actions will be taken. Interventions or corrective actionwill typically be taken when the value for the intermediate/final outputexceeds the upper or lower control limit defined for that measurement.However, SPC control has limited impact on improving or optimizing themanufacturing process, because intervention/correction only occurs whenan upper/lower control limit is exceeded. Adjustments are not usuallymade when the process is in control. Further, SPC evaluates a singlestation in isolation and does not consider trends across many stationsor the impact of several stations together on the final product.

Accordingly, new mechanisms are needed that can consider the inputs toand outputs of each station individually, and together with the otherinputs to and outputs of the other stations in the manufacturingprocess, to intelligently and dynamically adjust inputs to a stationcontroller to better control an associated station's operation. Inparticular, new mechanisms are needed to predict inputs that willoptimize the manufacturing process to produce in specification finaloutputs. New mechanisms are also needed to predict inputs that willimprove upon the design and manufacturing process of the final outputs.In addition, new mechanisms are needed to decrease variability in themanufacturing process and the final output.

Aspects of the disclosed technology address the foregoing limitations ofconventional manufacturing processes by providing mechanisms (which caninclude systems, methods, devices, apparatuses, etc.) for progressivelyimproving the manufacturing process and the resulting product ofmanufacture without disrupting the ongoing manufacturing process(referred to herein as predictive process control). As such, thedisclosed technology can be used to retrofit and integrate with existingmanufacturing systems and infrastructure, without causing disruptions toongoing manufacturing processes. Improvements are realized by providingdynamic control to one or more process stations in the manufacturingprocess, via conventional station controllers, to: (1) consistentlyproduce final outputs that are within specification; (2) optimize thedesign and manufacturing process of the final output; and (3) decreasevariability in the manufacturing process and the final output.

A deep learning controller based on machine-learning/artificialintelligence (AI) models may be used to evaluate control/station/processvalues and intermediate and final output values and determineadjustments to a station controller's input. As understood by those ofskill in the art, machine learning based techniques can vary dependingon the desired implementation, without departing from the disclosedtechnology. For example, machine learning techniques can utilize one ormore of the following, alone or in combination: hidden Markov models;recurrent neural networks; convolutional neural networks (CNNs); deeplearning; Bayesian symbolic methods; reinforcement learning, generaladversarial networks (GANs); support vector machines; image registrationmethods; applicable rule-based system.

Machine learning models can also be based on clustering algorithms(e.g., a Mini-batch K-means clustering algorithm), a recommendationalgorithm (e.g., a Miniwise Hashing algorithm, or EuclideanLocality-Sensitive Hashing (LSH) algorithm), and/or an anomaly detectionalgorithm, such as a Local outlier factor. The machine learning modelscan be based on supervised and/or unsupervised methods.

Machine learning models, as discussed herein, can also be used todetermine the process stations, control/station/process values andintermediate output values that are most influential on the final outputvalue (“key influencers”), and to optimize the manufacturing process bytargeting the key influencers.

FIG. 2 illustrates an example deep learning controller 218 that can beconfigured to control any number of (referred to herein by “n”)processing stations in a manufacturing process using predictive processcontrol, as discussed later in connection with FIGS. 4-7 . In FIG. 2 ,the n processing stations of a manufacturing process are represented byprocess stations 222 and 242. The process stations can operate seriallyor in parallel. Universal inputs 236, experiential priors 239,functional priors 238, and inputs from each of the n stations (e.g., 222and 242) can be provided to deep learning controller 218.

Functional priors, as used herein, refers to information relating to thefunctionality and known limitations of each process station,individually and collectively, in a manufacturing process. Thespecifications for the tools/equipment used at the process station areall considered functional priors. Example functional priors can include,but are not limited to: a screw driven extruder that has a minimum andmaximum speed that the screw can rotate; a temperature control systemthat has a maximum and minimum temperature achievable based on itsheating and cooling capabilities; a pressure vessel that has a maximumpressure that it will contain before it explodes; a combustible liquidthat has a maximum temperature that can be reached before combustion.Functional priors can also include an order in which the individualstations that are part of a manufacturing process perform theirfunctions. In some embodiments, several stations can run in parallel andcombine their intermediate outputs at a subsequent station.

Experiential priors, as used herein, refers to information gained byprior experience with, for example performing the same or similarmanufacturing process; operating the same or similar stations; producingthe same or similar intermediate/final outputs. In some embodiments,experiential priors can include acceptable final output values orunacceptable final output values. Acceptable final output values referto an upper limit, lower limit or range of final output values where thefinal output is considered “in specification.” In other words,acceptable final output values describe the parameters for final outputvalues that meet design specification, i.e., that are in-specification.Conversely, unacceptable final output values refer to upper/lower limitsor range of final output values where the final output is “not inspecification” (i.e., describe the parameters for final output valuesthat do not meet design specifications). For example, based on priorexperience it might be known that an O-ring used to seal pipes, willonly seal if it has certain compression characteristics. Thisinformation can be used to establish acceptable/unacceptable compressionvalues for an O-ring final output. In other words, all O-ring finaloutputs that have acceptable compression values are able to performtheir sealing functionality, while all O-ring final outputs that haveunacceptable compression values cannot perform their sealingfunctionality. Acceptable intermediate output values, which can bedefined per station, refer to upper/lower limits or a range ofintermediate output values that define the parameters for anintermediate output that can ultimately result in a final output that isin specification, without requiring corrective action by other stations.Unacceptable intermediate output values, which can also be defined bystation, refer to upper/lower limits or range of intermediate outputvalues that define the parameters for an intermediate output that willultimately result in a final output that is not in specification, unlesscorrective action is taken at another station. Similarly,acceptable/unacceptable parameters can be defined for other variablesrelating to the manufacturing process:

Acceptable upper/lower limits or range of values, definedcontrol/station/setpoint per station for each type of control/stationvalues value and setpoint, that define the parameters for, or are anindication of, satisfactory station performance. Satisfactoryperformance refers to (1) the performance of the station itself (e.g.,throughput rate is not too slow, there is no outage, noxious fumes orother harmful condition, resources are being used efficiently); and/or(2) control/station/setpoint values that cause an in specification finaloutput to be achievable, without requiring corrective action by otherstations. Unacceptable upper/lower limits or range of values, definedcontrol/station/setpoint per station for each type of control/stationvalues value and setpoint, that define the parameters for, or are anindication of, unsatisfactory station performance. Unsatisfactoryperformance refers to (1) the performance of the station itself (e.g.,throughput rate is too slow, an outage, noxious fumes or other harmfulstation condition, resources are not being used efficiently); and/or (2)control/station/setpoint values that cause an in specification finaloutput to be unachievable, unless corrective action by other stations istaken. Acceptable upper/lower limits or range of values for each processtype of process value, that define the performance parameters for, orare an indication of, satisfactory performance of the manufacturingprocess. Satisfactory performance refers to (1) the functioning of theprocess itself (e.g., throughput rate is not too slow, there is nooutage, noxious fumes or other harmful condition, resources are beingused efficiently); and/or (2) process values that cause an inspecification final output to be achievable. Unacceptable upper/lowerlimits or range of values, defined process for each type of processvalue, that define the performance parameters for, or are an indicationof, unsatisfactory process performance. Unsatisfactory performancerefers to (1) the process performance itself (e.g., throughput rate istoo slow, there is an outage, noxious fumes or other harmful condition,resources are not being used efficiently); and/or (2) process valuesthat cause an in specification final output to be unachievable.

Experiential priors can also include acceptable and unacceptablemanufacturing performance metrics. Manufacturing performance metricscalculate one or more aspects of multiple iterations of themanufacturing process (e.g., production volume for a specified timeperiod, production downtime for a specified time period, resources usedfor a specified time period or a specified number of final outputs,percentage of products not in specification for a specified time period,production volume for a particular operator, material costs associatedwith a specified number of final outputs).

Universal inputs, as used herein, refers to a value that is not specificto a particular process station, but rather to an aspect of the entiremanufacturing process, for example, a date, time of day, ambienttemperature, humidity or other environmental conditions that mightimpact the manufacturing process, operator, level of skill of theoperator, raw materials used in the process, raw material specificationssuch as color, viscosity, particle size, among other characteristicsthat are specific to the raw material, specific lot numbers and cost ofraw materials, tenure of the equipment/tools for each station,identifying information such as production work order numbers, batchnumbers, lot numbers, finished product numbers and finished productserial numbers.

Note, that the examples provided for each of functional priors,experiential priors and universal inputs represent one way to classifythese examples, other suitable classifications can be used. For example,another way to classify the input that is provided to deep learningcontroller 218 is: pre-process inputs (e.g., experiential priors,functional priors, material properties, scheduling requirements);in-process inputs (e.g., universal inputs, control values, stationvalues, intermediate values, final output values, process values);post-process inputs (e.g., manufacturing performance metrics and otheranalytics).

Each process station can be controlled by one or more associated stationcontrollers (e.g., station controller 220 controls process station 222and station controller 240 controls process station 242). In otherembodiments a single station controller can control multiple processstations. Deep learning controller 218 can provide control inputs(represented by 226 and 246) based on predictive process control to eachprocess station controller. In response to the received control input(e.g., 226 and 246), each station controller can provide one or morecontrol signals (e.g., 221 and 241) that provides commands forregulating a station's control values (e.g., control values 225 and245). Each station outputs an intermediate output (e.g., 224 and 244),that has an intermediate output value (234 a and 244 a respectively).All intermediate output values and the final output value from theprocessing stations are provided to deep learning controller 218. Eachstation also outputs station values (e.g., 228 and 248) to deep learningcontroller 218. FIG. 2 also shows that intermediate output 224 is sent(step 250) to 1 to n subsequent stations, which can represent a singlestation or n multiple stations. Station 242, as shown in FIG. 2 , canreceive (step 260) an intermediate input from 1 to n prior stations.

It is understood that the communication among deep learning controller218, the station controllers and process stations can use any suitablecommunication technologies that provide the ability to communicate withone or more other devices, and/or to transact data with a computernetwork. By way of example, implemented communication technologies caninclude, but are not limited to: analog technologies (e.g., relaylogic), digital technologies (e.g., RS232, ethernet, or wireless),network technologies e.g., local area network (LAN), a wide area network(WAN), the Internet, Bluetooth technologies, Nearfield communicationtechnologies, Secure RF technologies, and/or any other suitablecommunication technologies.

In some embodiments, operator inputs can be communicated to deeplearning controller 218, and/or any of the station controllers orprocess stations using any suitable input device (e.g., keyboard, mouse,joystick, touch, touch-screen, etc.).

In some embodiments, one or more process stations can be operatedmanually, for example, by a human operator performing specificinstructions. Instead of an electronic station controller, an operatorfollows a set of instructions, which can be provided manually or viaelectronic means (e.g., via a video or computer display). For example,at a manual station, an operator can perform the functions of cuttingwire to a specific length and measuring the length of the cut wire.Manual feedback, such as the length of the cut wire, can be provided todeep learning controller 218. Using predictive process control, asdescribed herein, deep learning controller 218 can determine whether thewire was cut to the desired length specification and provideimprovements to the cutting process, for example, that are provided inthe form of a set of instructions to the operator of the manual station.

FIG. 3 provides a process 300 for conditioning (training) a deeplearning controller 218, according to some embodiments of the disclosedsubject matter.

In step 310, the setpoints, algorithms and other control inputs for eachstation controller in a manufacturing process can be initialized usingconventional methods. Further, the control algorithms/operator canprovide initial control/station values. The control algorithms, initialsetpoint values, and initial control/station values can be provided todeep learning controller 218 (step 315).

Note, that control values, control algorithms, setpoints and any otherinformation (e.g., process timing, equipment instructions, alarm alerts,emergency stops) provided to a station controller will be referred tocollectively as “station controller inputs” or “control inputs.”

In addition, other inputs, like functional priors 238, experientialpriors 239 and universal inputs 236 can be provided to deep learningcontroller 218.

In step 325, the manufacturing process iterates through all the processstations using conventional control methods. As discussed above, theprocess stations discussed herein can operate in series or in parallel.Further, a single station can perform: a single process step multipletimes (sequentially or non-sequentially), or different process steps(sequentially or non-sequentially) for a single iteration of amanufacturing process. The process stations generate intermediateoutputs, or a final output if it is a final station. The intermediateoutput is transmitted to subsequent (downstream) station(s) in themanufacturing process until a final output is generated.

As the process iterates through each station, all the values associatedwith: an individual station (e.g., control values); an output of anindividual station (e.g., station values, intermediate/final outputvalues), or multiple stations (e.g., process values) are measured orcalculated and provided to condition the machine learning algorithms ofdeep learning controller 318 (steps 327 and 328).

In some embodiments manufacturing performance metrics (e.g., productionvolume for a specified time period, production downtime for a specifiedtime period, resources used for a specified time period or a specifiednumber of final outputs, percentage of products not in specification fora specified time period, production volume for a particular operator,material costs associated with a specified number of final outputs) forthe manufacturing process under conventional control can be calculatedand provided to deep learning controller 218 (step 329).

Although not shown, any actions taken (or control signals generated) bythe station controller in response to a received control value or othercontrol input from a process station can be provided to deep learningcontroller 218. Such actions can include adjusting temperature, speed,etc. In addition, deviations from: acceptable setpoints, acceptableintermediate/final output values, acceptable control/station/processvalues can also be calculated and provided to deep learning controller218.

Note all inputs to deep learning controller 218 can be enteredelectronically or via manual means by an operator.

The conditioning of the machine learning models of deep learningcontroller 218 (step 235) can be achieved through unsupervised learningmethods. Other than functional priors 238, experiential priors 239,universal inputs 236 that are input into deep learning controller 218,deep learning controller 218 draws inferences simply by analyzing thereceived data that it collects during the iteration of the manufacturingprocess (e.g., steps 328 and 329). In other embodiments, deep learningcontroller 218 can be conditioned via supervised learning methods, or acombination of supervised and unsupervised methods or similar machinelearning methods. Further, the training of deep learning controller 218can be augmented by: providing deep learning controller 218 withsimulated data or data from a similar manufacturing process. In oneembodiment, deep learning controller 218 can be conditioned byimplementing deep learning controller 218 into a similar manufacturingprocess and fine-tuning the deep learning controller duringimplementation in the target manufacturing process. That is, training ofdeep learning controller 218 can be performed using a training processthat is performed before deep learning controller 218 is deployed into atarget manufacturing environment.

Based on the conditioning of the machine learning models, deep learningcontroller 218 can predict the values for the characteristics of thefinal output (“expected value” or “EV”) that determine whether or notthe final output value will be acceptable or not (i.e., whether or notthe final output is “in specification”) (step 342). Deep learningcontroller 218 can provide a confidence level for its prediction at aninstant or over a specific time period, for example, to provide ameasure of statistical confidence in the prediction. In some aspects,the confidence level may be expressed as a numerical probability ofaccuracy for the prediction, in other aspects, the confidence level maybe expressed as an interval or probability range. At step 343, deeplearning controller 218 can compare the expected value to the actualmeasurements of the specified characteristics of the final output(“actual value” or “AV”).

In some aspects, deep learning controller 218 can be configured toperform EV predictions regarding output characteristics on astation-by-station basis. That is, deep learning controller 218 can makeEV predictions regarding outputs at a specific station, and subsequentlycompare those predictions to actual outputs observed at that station.Alternatively, EV predictions can be made for outputs resulting fromcombined processing performed by two or more stations, depending on thedesired implementation.

As the manufacturing process proceeds through each station, and deeplearning controller 218 receives additional information, deep learningcontroller 218 can revise its expected value, along with the confidencelevel. If deep learning controller 218's predictions are correct, over aspecified time period and with a predefined threshold confidence level,then deep learning controller 218 can provide a signal that deeplearning controller 218 is ready to control the operation of the processstations.

In some embodiments, deep learning controller 218 can also, afterinitialization of the station controllers, at the outset of an iterationof the manufacturing process (i.e., proceeding through all stations inthe manufacturing process), as well as over the course of themanufacturing process, predict whether any control inputs will causeunsatisfactory station performance or impact process performance (i.e.,cause unacceptable process performance). Deep learning controller 218can provide a confidence level for its predictions. Deep learningcontroller 218 can determine whether or not deep learning controller218's predictions were correct. In further embodiments, if deep learningcontroller 218's predictions, both with respect to the expected finaloutput value and predicted station/process performance, over a specifiedtime period and with a threshold confidence level, as defined by anoperator, then deep learning controller 218 can provide a signal thatdeep learning controller 218 is ready to control the operation of theprocess stations.

FIG. 4 , shows an example process for controlling the manufacturingprocess using predictive process control.

Deep learning controller 218 uses its conditioned machine learningalgorithms (as discussed in connection with FIG. 3 ) to calculatecontrol inputs for the station controllers associated with the processstations of a manufacturing process. Based on the calculated controlinputs, deep learning controller 218 can predict the expected value (EV)of the final output for the manufacturing process, along with aconfidence level for its prediction (step 405). If deep learningcontroller 218 determines, with a threshold confidence level, that theexpected value will be in specification (step 415), then deep learningcontroller 218 can output the calculated control inputs to the stationcontrollers associated with the process stations of the manufacturingprocess (step 420). Deep learning controller 218 can calculate andprovide the calculated control inputs to one or more station controllersat the outset or continuously throughout the manufacturing process. Thecontrol inputs do not have to be provided to the station controllers inserial order, but can be provided to one or more station controllers inparallel or in any order that is suitable to produce final outputs thatare in specification. Each station controller that receives controlinputs from deep learning controller 218 can send control signals to itsassociated station to control the control values (e.g., control thecontrol values so that they match the received setpoints). The machinelearning algorithms continue to be improved throughout theimplementation of PPC (step 335). Further, the functional andexperiential priors can be dynamically updated throughout PPC.

At step 430, the manufacturing process proceeds through all the processstations serially or in parallel. As the process iterates through eachstation, all the values associated with: an individual station (e.g.,control values); an output of an individual station (e.g., stationvalues, intermediate/final output values), or multiple stations (e.g.,process values) can be measured or calculated and provided to conditionthe machine learning algorithms of deep learning controller 218 (steps432). Further, manufacturing performance metrics for the manufacturingprocess under predictive process control can be calculated and providedto deep learning controller 218 (step 432). The process values andmanufacturing performance metrics calculated under PPC can be comparedto the process values and manufacturing performance metrics calculatedunder conventional control to determine the improvements provided bypredictive process control.

Throughout the process shown in FIG. 4 , deep learning controller 218can predict the expected value (EV) for the final output, determinewhether the expected value for the final output is in-specification,determine the confidence level for its prediction, and then providefeedback on its prediction by comparing the expected final value to theactual final value (step 445). Further, if deep learning controller 218determines that the final output is not in-specification, it cancalculate adjustments to the control inputs, so that the predictedexpected value for the final output is in-specification.

In some aspects, deep learning controller 218 can be configured toperform EV predictions regarding intermediate outputs on astation-by-station basis. That is, deep learning controller 218 can makeEV predictions regarding outputs at a specific station, determinewhether the EV for an intermediate output is in-specification, determinethe confidence level for its prediction, and subsequently compare thosepredictions to actual outputs observed at that station. Alternatively,EV predictions can be made for outputs resulting from combinedprocessing performed by two or more stations, depending on the desiredimplementation. Further, if deep learning controller 218 determines thatthe intermediate output is not in-specification, it can calculateadjustments to the control inputs, so that the predicted expected valuefor the intermediate output is in-specification.

Note, if the confidence level determined by deep learning controller 218is below a predetermined threshold, then control of the manufacturingprocess can revert back to conventional control, as described inconnection with FIG. 3 .

In some embodiments, deep learning controller 218 can also monitorwhether any of the station/control/process or intermediate output valuesare unacceptable and make further adjustments to the station controllerinputs or generate an alert if the problem cannot be fixed by adjustingthe station controller inputs.

Based on data it receives as the stations run through the manufacturingprocess, deep learning controller 218 can adjust the control inputs forone or more station controller. In further embodiments, deep learningcontroller 218 can, not only initialize the station controller inputsbefore the start of an iteration through the process stations of themanufacturing process, but also adjust station controller inputs duringthe process itself (“feedforward control”). In particular, based oninformation received from prior stations in a manufacturing process,deep learning controller 218 can make changes to the control inputsassociated with later stations in the process. For example, if deeplearning controller 218 determines that there are defects in theintermediate output of a particular station, then deep learningcontroller 218 can determine whether there are any corrective actionsthat can be taken at subsequent stations, so that the final output willbe in specification. Deep learning controller 218 can also make changesto the current and prior processing stations in real-time, based onfeedback about control/station/process values and/or intermediate/finaloutput values (“feedback control”). This ability to dynamically controleach station in real time and make adjustments to downstream stationcontrollers to compensate for errors, miscalculations, undesirableconditions or unforeseen outcomes that occurred upstream, increases thelikelihood of manufacturing in specification final outputs. Further,even though a broad range of final output values can be considered inspecification, it may be desirable to manufacture final outputs that areof the same or similar quality and have similar final output valueswithin a narrower range of acceptable final output values. Feedback andfeedforward control, along with the deep learning controller'spredictive capabilities, enable deep learning controller 218 to adjustthe station controllers to produce final output values of consistentquality and similar final output values.

It is useful to identify what parameters of the manufacturing processmost impact the final output value or the process performance (the “keyinfluencers”). Deep learning controller 218 can consider all parametersof the manufacturing process (e.g., one or more control values, one ormore station values, one or more process values, one or more stations,one or more intermediate outputs or any combination thereof), and usingone or more of its machine learning algorithms can identify the keyinfluencers. In some aspects, deep learning controller 218 can employunsupervised machine learning techniques to discover one or more keyinfluencers, for example, wherein each key influencer is associated withone or more parameters (or parameter combinations) that affectcharacteristics of various station outputs, the final output, and/orprocess performance. It is understood that discovery of key influencersand their associated parameters may be performed through operation andtraining of deep learning controller 218, without the need to explicitlylabel, identify or otherwise output key influencers or parameters to ahuman operator.

In some approaches, deep learning controller 218 can rank, in order ofsignificance, the impact of each parameter of the manufacturing processon the final output value or the process performance. A key influencer,can be identified based on: a cutoff ranking (e.g., the top 5 aspects ofthe manufacturing process that impact the final output value), a minimumlevel of influence (e.g., all aspects of the manufacturing process thatcontribute at least 25% to the final output value); or any othersuitable criteria. In some aspects, key influence characteristics may beassociated with a quantitative score, for example, that is relative tothe weight of influence for the corresponding characteristic.

Deep learning controller 218 can continuously, throughout themanufacturing process, calculate the key influencers (step 446).

In some embodiments, as described in connection with FIG. 5 , the keyinfluencers can be used to help build a more robust data set to traindeep learning controller 218.

In conventional manufacturing, the data generated from the manufacturingprocess is limited, because the goal is to produce intermediate outputvalues that are within a specified standard variation from the mean. Asa consequence, the range of control inputs, control/station/processvalues are also limited, because they are all designed to produceintermediate output values that are within a specified range from themean. In contrast, under the subject disclosure, as long as the finaloutput is in specification, the intermediate output values do not haveto be within a specific range from the mean. As the deep learningcontroller's predictions become more accurate, in some embodiments, deeplearning controller 218 can intentionally make changes to controlinputs, to create the conditions for generating intermediate outputvalues that may exceed the normal fluctuations of an in control processunder traditional manufacturing process control (e.g., SPC), but stillgenerates final outputs that are in specification. This creates a morerobust data training set for deep learning controller 218 to detectpatterns and determine how particular stations, station/control/processvalues, and intermediate output values impact the final output value(e.g., whether the final output is in specification or not). Thecreation of a robust data set can occur both during and prior toimplementation of deep learning controller 218 in a productionenvironment.

FIG. 5 , shows an example process for creating a more robust data set.In some embodiments, deep learning controller 218 can adjust knowncontroller inputs (e.g., control setpoints) to one or more stationcontrollers to produce intermediate output values that may exceed aspecified range from the mean. For example, once deep learningcontroller 218 is conditioned (step 335), it knows at least some controlinputs for each station controller that will result in final outputvalues that are in specification. In some embodiments, deep learningcontroller 218 can select one or more station controllers and vary theknown control inputs (e.g., setpoints) to the selected stationcontroller(s) by a predetermined threshold (e.g., new setpoint=originalsetpoint+1% original setpoint) (step 510). Deep controller 218 canpredict the expected value (EV) of the final output for themanufacturing process using the newly calculated control inputs, alongwith a confidence level for its prediction (step 515). If deep learningcontroller 218 determines, with a threshold confidence level, that theexpected value will be in specification (step 517), then deep learningcontroller 218 can provide the adjusted control inputs to the selectedstation controllers (step 520). Deep learning controller 218 can alsocompare the predicted expected values with the actual final outputvalues (AV) to provide feedback on its predictions and to further adjustthe control inputs (step 525).

In one example embodiment, control inputs related to material tolerancescan be purposely varied, and deep learning controller 218 can be used todetermine what adjustments to make to other control inputs to produce inspecification final products. By training deep learning controller 218in this manner, when new materials are introduced into the manufacturingprocess unexpectedly, deep learning controller 218 can adapt the controlinputs on its own, without requiring operator input. Similarly,adjustments can purposely be made to control algorithms (e.g.,adjustments that mimic a virus or other attack), and deep learningcontroller 218 can be used to determine what adjustments to make toother control inputs to produce in specification final products. Bypurposely introducing these changes, deep learning controller 218 canadapt the control inputs on its own, without requiring operator input,when adjustments are made to the control algorithms unexpectedly duringthe manufacturing process.

In some embodiments, deep learning controller 218 can first determinethe key influencers (step 446 as described in connection with FIG. 4 )and vary the control inputs associated with the key influencers. Inother embodiments, deep learning controller 218 can vary control inputsfor all the station controllers, or select station controllers accordingto a predetermined formula. To create a robust data set, deep learningcontroller 218 can continue to adjust the control inputs associated withspecific station controllers each time it iterates through themanufacturing process (step 528). For example, deep learning controller218 can adjust the setpoints associated with the key influencers by 1%and iterate through the manufacturing process one or more times. On asubsequent iteration, deep learning controller 218 can adjust thesetpoints associated with the key influencers by another 1%, and iteratethrough the manufacturing process one or more times. Deep learningcontroller 218 can keep making adjustments, as long as the expectedvalues for the manufacturing process using the adjusted setpoints willresult in final output values that are in specification with a thresholdconfidence level. During each iteration, as described in connection withFIG. 4 , station/control values, intermediate/final output values,process values and manufacturing performance metrics are generated (step430) and used to condition the machine learning algorithms of deeplearning controller 218 (step 335), and to dynamically update thefunctional and experiential priors.

The following example, further illustrates creating a robust data set byvarying a control input (e.g., a temperature setpoint for a particularstation). In this example, the setpoint temperature for a specificstation is 95°, and the actual temperature of the station fluctuatesbetween 92°-98° (i.e., 3° above and below the setpoint temperature). Thesetpoint temperature at 95° and the corresponding ±3° fluctuation of theactual station temperature all result in final output values that are inspecification. Deep learning controller 218 can then predict ifadjusting the temperature setpoint by a negligible amount (e.g., ±0.5°),will still result in final output values that are in specification. Ifdeep learning controller 218 predicts in specification final outputvalues, at a threshold confidence level, for a manufacturing processusing adjusted temperature setpoints, then deep learning controller 218will adjust the temperature setpoint by ±0.5°. Assuming the same stationtemperature fluctuation of ±3° from the setpoint, then the actualstation temperature, will range from 92.5°-98.5°, when the setpoint is95.5°; and will range from 91.5°-97.5° when the setpoint is 94.5°. Deeplearning controller 218 can compare the final output value with expectedoutput value over the temperature range 91.5°-98.5°, and determinewhether it correctly predicted that the final output value would be inspecification. Because the temperature setpoint was adjusted by ±0.5°,the resulting data set covers a broader temperature range: 91.5°-98.5than the original temperature range of 92°-98°.

Note, that instead of changing station controller setpoints, controlvalues of a station can be changed by modifying other control inputs tothe station controller (e.g., control values) that would achieve thesame goal of changing the setpoints. For example, assume the following:setpoint for a station controller is 100 degrees, actual stationtemperature value (i.e., a control value) is 100 degrees, and the goalis to increase the actual temperature value of the station by twodegrees. Instead of increasing the temperature setpoint to 102 degreesto achieve that change, deep learning controller 218 can change thecontrol value that it provides to the station controller by two degreesbelow the actual temperature value (e.g., changing the control valuefrom 100 degrees to 98 degrees), causing the station controller toincrease the station temperature by two degrees to achieve the desiredstation temperature increase of two degrees (i.e., 102 degrees). It maybe necessary to change control values, instead of setpoints, whenexisting station controls do not allow setpoints to be changed.

In some embodiments, as described in connection with FIG. 6 , the keyinfluencers can be used to optimize the final output or process values.

Once the most important stations, station/control/process values,intermediate output values that influence the final output value or theprocess performance are identified, then resource allocation and processoptimization can target the key influencers. For example, instead ofcollecting volumes of data that marginally impact the process, dataresources (e.g., collection, processing and storage) can be allocatedlargely for the data associated with the key influencers (“curateddata”). Further, the curated data (a subset of all the data availablefrom the manufacturing process) can be provided to machine learningalgorithms to make optimizations to the key influencers, reducing thevolume of training examples and increasing the resources available toprocess the curated data. In addition, the machine learning algorithmscan be directed to optimizing the key influencers, instead of the entireprocess, reducing the possible states and actions that a machinelearning algorithm must consider, and allocating resources moreefficiently and intelligently. For example, in reinforcement learning,state and action spaces define the range of possible states an agentmight perceive and the actions that are available to the agent. By usingreinforcement learning only on the key influencers, the state and actionspaces are reduced, making the algorithm more manageable.

In some embodiments, an operator can specify one or more characteristicsof the final output or the process values that it would like to optimizefor (e.g., using the least amount of power or resources, fastestthroughput, minimum number of defects, greatest tensile strength). Deeplearning controller 218 can run machine learning models likereinforcement learning, targeting the key influencers, to optimize forthe specified characteristics of the final output (“optimal designvalues”) and/or specified process values (“optimal process values”).

FIG. 6 , according to some embodiments of the subject disclosure, showsa process for specifying optimal design/process values, and using a deeplearning controller to control and optimize the key influencers toachieve the desired optimal design or process values.

At 600, the desired optimal design/process values are provided to deeplearning controller 218. For example, generate an in specification finaloutput: using the least amount of power or resources, fastestthroughput, minimum number of defects, greatest tensile strength, etc.

As shown at 605, deep learning controller 218 can determine the keyinfluencers for driving in specification products (see also FIG. 4 ),and can predict the control inputs (“optimal control inputs”) to controleach key influencer to achieve the desired optimal design or processvalues. Deep learning controller 218 can determine the expected value ofthe final output for the manufacturing process using the optimal controlinputs, and predict whether the expected value will be in specificationand achieve the desired optimal design or process values (step 615).Deep learning controller 218 can also calculate a confidence level thatthe optimal control inputs will result in optimal design or processvalues (step 615). Deep learning controller 218 can provide the optimalcontrol inputs to the relevant station controllers at the outset andcontinuously throughout the manufacturing process (step 620). Theoptimal control inputs do not have to be provided to the stationcontrollers in serial order, but can be provided to one or more stationcontrollers in parallel or in any order that is suitable to producefinal outputs that achieve the desired optimal design or process values.

In other embodiments, the parameters for final outputs that are inspecification will be updated to match the desired optimaldesign/process values. For example, if in specification tensile strengthfor a final output is 40-90 megapascals (MPa), and optimal tensilestrength for the final output is determined to be 70-90 MPa, then the inspecification parameters can be updated to 70-90 MPa. Deep learningcontroller 218 can predict, and determine the confidence level, that thecalculated control inputs for the station controllers associated withkey influencers will achieve the updated specification parameters (i.e.,70-90 MPa). Deep learning controller 218 can update the relevant stationcontrollers when it predicts that the calculated control inputs willachieve the updated specification parameters, at a confidence levelabove a predefined threshold.

Note, deep learning controller 218 can continue to provide controlinputs to the other station controllers as discussed in connection withFIG. 3 , but the optimization will just target the station controllersassociated with the key influencers. In some embodiments, deep learningcontroller 218 can optimize the station controllers associated with anystation, station/control/process value or intermediate output value, notjust the key influencers.

Deep learning controller 218 can also compare the expected values withthe actual values to provide feedback on its prediction and to furtheradjust the control inputs (step 545). As the process begins and goesthrough all the stations, in some embodiments, only measurements relatedto the key influencers, as well as measurements for the final output,will be collected and provided to deep learning controller 218. In otherembodiments, deep learning controller 218 can continue to collect datafrom all the process stations. The data will be used to continuouslyimprove the control inputs for the key influencers, so that the optimaldesign/process values are achieved consistently and with a highconfidence level.

Note that the desired optimal design/process values can be changed atany time. Further, new key influencers can be calculated by changing thecriteria for what qualifies as a key influencer (e.g., a first criteriamight classify as key influencers the top 5 stations that drivein-specification final outputs, while an updated criteria might classifyas key influencers only the top 3 stations that drive in-specificationfinal outputs). Further, once deep learning controller 218 predictscontrol inputs to achieve optimal design/process value with a certainconfidence level over a period of time, deep learning controller 218 canidentify which of the key influencers are key influencers for drivingthe desired optimizations and only target that subset of keyinfluencers, further reducing the possible actions/states that a machinelearning algorithm must consider and more efficiently allocating theresources to that subset.

In further embodiments, deep learning controller 218 can, not onlyinitialize the station controller inputs for the key influencers beforethe start of an iteration through the process stations of themanufacturing process, but also adjust the control inputs during theprocess itself. In particular, based on information received from priorstations in a process, deep learning controller 218 can make changes tothe control inputs associated with later stations in the process toensure the optimal design/process values are achieved. Deep learningcontroller 218, can also adjust prior stations in the process as itproceeds through the process and receives data from subsequent stations.

In some embodiments manufacturing performance metrics for themanufacturing process trying to achieve optimal design or process valuescan be calculated and provided to deep learning controller 218 (step632). These manufacturing performance metrics can be compared to themanufacturing performance metrics for PPC and/or the manufacturingperformance metrics for conventional control to determine theimprovements provided by optimizing the design and or process.

An example of manufacturing system optimization to reduce the possibleactions/states that can be adapted and implemented by deep learningcontroller 218 is described in U.S. Patent Provisional Application62/836,199 “Adaptive Methods and Real-Time Decision Making forManufacturing Control,” which is hereby incorporated by reference hereinin its entirety. The disclosed method is just an example and is notintended to be limiting.

Another example of manufacturing system optimization to reduce thepossible actions/states that a machine learning algorithm must considerthat can be adapted and implemented by deep learning controller 218 ofthe subject disclosure is described in U.S. Patent ProvisionalApplication 62/836,213 “TRANSFER LEARNING APPROACH TO MULTI-COMPONENTMANUFACTURING CONTROL,” which is hereby incorporated by reference hereinin its entirety. The disclosed method is just an example and is notintended to be limiting.

FIG. 7 illustrates an example manufacturing system that can applypredictive process control to optimize the design/process of a finaloutput. Specifically, a 3D manufacturing system can have a number ofprocess stations (e.g., stations 700-750), and each process station canperform a process step (e.g., deposit an extruded layer of material thatwill result in a final output). Using predictive process control, asdescribed in FIG. 6 , an operator can specify what it wants to optimizefor (e.g., the tensile strength of the final output) (step 600).Further, deep learning controller 218 can determine the key influencers(step 605) that impact the tensile strength of a final output (e.g.,changes to the volume of deposited material per layer and changes toextruder velocity control). Deep learning controller 218 can reduce thepossible actions/states that a machine learning algorithm must considerby running machine learning algorithms (e.g., reinforcement learning) tooptimize only the key influencers, and not optimize other controlattributes (e.g., extruder nozzle temperature, printing pattern of theprint head, etc. Further), deep learning controller 218 can identifywhich extruded layers (e.g., layers 4 and 5) have the most impact ontensile strength, and optimize the key influencers for the stations thatdeposit those layers (e.g., stations 4 and 5).

Note, in some embodiments, in connection with predictive process controlmethod discussed in FIGS. 4-7 , the conventional station controllers canbe turned off and deep learning controller 218 can control the processstations directly.

Further, in some embodiments of predictive process control, a datalogging module 810, as shown in FIG. 8 , can be configured to receivedata from deep learning controller 218, to analyze the data and togenerate reports, emails, alerts, log files or other data outputs (step815). For example, data logging module 810 can be programmed to searchthe received data for predefined triggering events, and to generatereports, emails, alerts, log files or other data outputs showingrelevant data associated with those triggering events (step 815). Forexample, adjusting a setpoint or other control input can be defined as atriggering event and the following data can be reported: name ofsetpoint or other control input that was adjusted, the station that wasaffected, date and time of day, the confidence level associated with theadjusted control input, whether the adjusted control input achievedin-specification final outputs or optimal design/process values, reasonsfor why the adjustment was made. In another example, achieving or notachieving optimal design/process values can be defined as triggeringevent and the following data can be reported: control inputs for the keyinfluencers, date and time of day, any reported outages, confidencelevel for the control inputs, throughput time, resources expended.associated operator. Other suitable triggers can be defined, and othersuitable data can be reported. The data logging module can also compareprocess values, final output values, manufacturing performance metricsthat were collected during the manufacturing process using conventionalcontrol with process values, final output values, manufacturingperformance metrics that were collected during the manufacturing processusing predictive performance control. In some embodiments, data loggingmodule 810 can be included within deep learning controller 218.

FIG. 9 shows the general configuration of an embodiment of deep learningcontroller 218 that can implement predictive process control, inaccordance with some embodiments of the disclosed subject matter.Although deep learning controller 218 is illustrated as a localizedcomputing system in which various components are coupled via a bus 905,it is understood that various components and functional computationalunits (modules) can be implemented as separate physical or virtualsystems. For example, one or more components and/or modules can beimplemented in physically separate and remote devices, such as, usingvirtual processes (e.g., virtual machines or containers) instantiated ina cloud environment.

Deep learning controller 218 can include a processing unit (e.g., CPU/sand/or processor/s) 910 and bus 905 that couples various systemcomponents including system memory 915, such as read only memory (ROM)920 and random access memory (RAM) 925, to processing unit 910.Processing unit 910 can include one or more processors such as aprocessor from the Motorola family of microprocessors or the MIPS familyof microprocessors. In an alternative embodiment, the processing unit910 can be specially designed hardware for controlling the operations ofdeep learning controller 218 and performing predictive process control.When acting under the control of appropriate software or firmware,processing module 910 can perform various machine learning algorithmsand computations of PPC described herein.

Memory 915 can include various memory types with different performance.characteristics, such as memory cache 912. Processor 910 can be coupledto storage device 930, which can be configured to store software andinstructions necessary for implementing one or more functional modulesand/or database systems. Each of these modules and/or database systemscan be configured to control processor 910 as well as a special-purposeprocessor where software instructions are incorporated into the actualprocessor design.

To enable operator interaction with deep learning controller 218, inputdevice 945 can represent any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input and so forth. An output device 935can also be one or more of a number of output mechanisms (e.g., printer,monitor) known to those of skill in the art. In some instances,multimodal systems can enable an operator to provide multiple types ofinput to communicate with deep learning controller 218. Communicationsinterface 940 can generally govern and manage the operator input andsystem output, as well as all electronic input received from and sent toother components that are part of a manufacturing process such as thestation controllers, process stations, data logging module, and allassociated sensors and image capturing devices. There is no restrictionon operating on any particular hardware arrangement and therefore thebasic features here may easily be substituted for improved hardware orfirmware arrangements as they are developed. Data output from deepcontroller 218 can be displayed visually, printed, or generated in fileform and stored in storage device 930 or transmitted to other componentsfor further processing.

Communication interface 940 can be provided as interface cards(sometimes referred to as “line cards”). Generally, they control thesending and receiving of data packets over the network and sometimessupport other peripherals used with the router. Among the interfacesthat can be provided are Ethernet interfaces, frame relay interfaces,cable interfaces, DSL interfaces, token ring interfaces, and the like.In addition, various very high-speed interfaces may be provided such asfast token ring interfaces, wireless interfaces, Ethernet interfaces,Gigabit Ethernet interfaces, ATM interfaces, HSSI interfaces, POSinterfaces, FDDI interfaces and the like. Generally, these interfacesmay include ports appropriate for communication with the appropriatemedia. In some cases, they may also include an independent processorand, in some instances, volatile RAM. The independent processors maycontrol such communications intensive tasks as packet switching, mediacontrol and management. By providing separate processors for thecommunications intensive tasks, these interfaces allow processing unit910 to efficiently perform machine learning and other computationsnecessary to implement predictive process control. Communicationinterface 940 can be configured to communicate with the other componentsthat are part of a manufacturing process such as the stationcontrollers, process stations, data logging module, and all associatedsensors and image capturing devices.

Sensors associated with a manufacturing process can include sensors thatexisted prior to implementation of PPC, as well as any new sensors thatwere added to perform any additional measurements used by PPC. One ormore sensors can be included within or coupled to each station. Sensorscan be used to measure values generated by a manufacturing process suchas: station values, control values, intermediate and final outputvalues. Further, information provided by sensors can be used by deeplearning controller 218, or by a module external to deep learningcontroller 218 to compute process values and performance manufacturingmetrics. Example sensors can include, but are not limited to: rotaryencoders for detecting position and speed; sensors for detectingproximity, pressure, temperature, level, flow, current and voltage;limit switches for detecting states such as presence or end-of-travellimits. Sensor, as used herein, includes both a sensing device andsignal conditioning. For example, the sensing device reacts to thestation or control values and the signal conditioner translates thatreaction to a signal that can be used and interpreted by deep learningcontroller. Example of sensors that react to temperature are′Ds,thermocouples and platinum resistance probes. Strain gauge sensors reactto pressure, vacuum, weight, change in distance among others. Proximitysensors react to objects when they are within a certain distance of eachother or a specified tart. With all of these examples, the reaction mustbe converted to a signal that can be used by deep learning controller218. En many cases the signal conditioning function of the sensorsproduce a digital signal that is interpreted by deep learning controller218. The signal conditioner can also produce an analog signal or TTLsignal among others.

In some embodiments, deep learning controller 218 can include an imagingprocessing device 970 that processes images received by various imagecapturing devices such as video cameras, that are coupled one or moreprocessing station and are capable of monitoring and capturing images ofintermediate and final outputs. These images can be transmitted to deeplearning controller 218 via communication interface 940, and processedby image processing device 970. The images can be processed to providedata, such as number and type of defects, output dimensions, throughput,that can be used by deep learning controller 218 to compute intermediateand final output values. In some embodiments, the image processingdevice can be external to deep learning controller 218 and provideinformation to deep learning controller 218 via communication interface940.

Storage device 930 is a non-transitory memory and can be a hard disk orother types of computer readable media that can store data accessible bya computer, such as magnetic cassettes, flash memory cards, solid statememory devices, digital versatile disks, cartridges, random accessmemories (RAMs) 825, read only memory (ROM) 820, and hybrids thereof.

In practice, storage device 930 can be configured to receive, store andupdate input data to and output data from deep learning controller 218,for example functional priors, experiential priors, universal input;pre-process inputs; in-process inputs and post-process inputs.

In some embodiments, any suitable computer readable media can be usedfor storing instructions for performing the functions and/or processesdescribed herein. For example, in some embodiments, computer readablemedia can be transitory or non-transitory. For example, non-transitorycomputer readable media can include media such as non-transitorymagnetic media (such as hard disks, floppy disks, etc.), non-transitoryoptical media (such as compact discs, digital video discs, Blu-raydiscs, etc.), non-transitory semiconductor media (such as flash memory,electrically programmable read only memory (EPROM), electricallyerasable programmable read only memory (EEPROM), etc.), any suitablemedia that is not fleeting or devoid of any semblance of permanenceduring transmission, and/or any suitable tangible media. As anotherexample, transitory computer readable media can include signals onnetworks, in wires, conductors, optical fibers, circuits, and anysuitable media that is fleeting and devoid of any semblance ofpermanence during transmission, and/or any suitable intangible media.

The various systems, methods, and computer readable media describedherein can be implemented as part of a cloud network environment. Asused in this paper, a cloud-based computing system is a system thatprovides virtualized computing resources, software and/or information toclient devices. The computing resources, software and/or information canbe virtualized by maintaining centralized services and resources thatthe edge devices can access over a communication interface, such as anetwork. The cloud can provide various cloud computing services viacloud elements, such as software as a service (SaaS) (e.g.,collaboration services, email services, enterprise resource planningservices, content services, communication services, etc.),infrastructure as a service (IaaS) (e.g., security services, networkingservices, systems management services, etc.), platform as a service(PaaS) (e.g., web services, streaming services, application developmentservices, etc.), and other types of services such as desktop as aservice (DaaS), information technology management as a service (ITaaS),managed software as a service (MSaaS), mobile backend as a service(MBaaS), etc.

The provision of the examples described herein (as well as clausesphrased as “such as,” “e.g.,” “including,” and the like) should not beinterpreted as limiting the claimed subject matter to the specificexamples; rather, the examples are intended to illustrate only some ofmany possible aspects. A person of ordinary skill in the art wouldunderstand that the term mechanism can encompass hardware, software,firmware, or any suitable combination thereof.

Unless specifically stated otherwise as apparent from the abovediscussion, it is appreciated that throughout the description,discussions utilizing terms such as “determining,” “providing,”“identifying,” “comparing” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system memories or registersor other such information storage, transmission or display devices.Certain aspects of the present disclosure include process steps andinstructions described herein in the form of an algorithm. It should benoted that the process steps and instructions of the present disclosurecould be embodied in software, firmware or hardware, and when embodiedin software, could be downloaded to reside on and be operated fromdifferent platforms used by real time network operating systems.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored on acomputer readable medium that can be accessed by the computer. Such acomputer program may be stored in a computer readable storage medium,such as, but is not limited to, any type of disk including floppy disks,optical disks, CD-ROMs, magnetic-optical disks, read-only memories(ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic oroptical cards, application specific integrated circuits (ASICs), or anytype of non-transient computer-readable storage medium suitable forstoring electronic instructions. Furthermore, the computers referred toin the specification may include a single processor or may bearchitectures employing multiple processor designs for increasedcomputing capability.

The algorithms and operations presented herein are not inherentlyrelated to any particular computer or other apparatus. Variousgeneral-purpose systems may also be used with programs in accordancewith the teachings herein, or it may prove convenient to construct morespecialized apparatus to perform the required method steps andsystem-related actions. The required structure for a variety of thesesystems will be apparent to those of skill in the art, along withequivalent variations. In addition, the present disclosure is notdescribed with reference to any particular programming language. It isappreciated that a variety of programming languages may be used toimplement the teachings of the present disclosure as described herein,and any references to specific languages are provided for disclosure ofenablement and best mode of the present disclosure.

The logical operations of the various embodiments are implemented as:(1) a sequence of computer implemented steps, operations, or proceduresrunning on a programmable circuit within a general use computer, (2) asequence of computer implemented steps, operations, or proceduresrunning on a specific-use programmable circuit; and/or (3)interconnected machine modules or program engines within theprogrammable circuits. The system 300 can practice all or part of therecited methods, can be a part of the recited systems, and/or canoperate according to instructions in the recited non-transitorycomputer-readable storage media. Such logical operations can beimplemented as modules configured to control the processor 363 toperform particular functions according to the programming of the module.

It is understood that any specific order or hierarchy of steps in theprocesses disclosed is an illustration of exemplary approaches. Basedupon design preferences, it is understood that the specific order orhierarchy of steps in the processes may be rearranged, or that only aportion of the illustrated steps be performed. Some of the steps may beperformed simultaneously. For example, in certain circumstances,multitasking and parallel processing may be advantageous. Moreover, theseparation of various system components in the embodiments describedabove should not be understood as requiring such separation in allembodiments, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

The predictive process control apparatus, method and system have beendescribed in detail with specific reference to these illustratedembodiments. It will be apparent, however, that various modificationsand changes can be made within the spirit and scope of the disclosure asdescribed in the foregoing specification, and such modifications andchanges are to be considered equivalents and part of this disclosure.

STATEMENTS OF THE DISCLOSURE

Statement 1: A computer-implemented method comprising: receiving aplurality of control values from two or more stations, at a deeplearning controller, wherein the control values are generated at the twoor more stations deployed in a manufacturing process; predicting, by thedeep learning controller, an expected value for an intermediate or finaloutput of an article of manufacture, based on the control values;determining, by the deep learning controller, if the predicted expectedvalue for the article of manufacture is in-specification; andgenerating, by the deep learning controller, one or more control inputsif the predicted expected value for the article of manufacture is notin-specification, wherein the one or more control inputs are configuredto cause an intermediate or final output for the article of manufactureto be in-specification.

Statement 2: The computer-implemented method of statement 1, furthercomprising: outputting the one or more control inputs to one or morestation controllers, if a confidence level associated with the one ormore control inputs exceeds a predetermined threshold.

Statement 3: The computer-implemented method of any of statements 1-2,wherein outputting the one or more control inputs to the one or morestation controllers, further comprises: providing a first control inputto a first station controller associated with a first station; andproviding a second control input to a second station controllerassociated with a second station, and wherein the second station isdownstream from the first station in the manufacturing process.

Statement 4: The computer-implemented method of any of statements 1-3,wherein predicting the intermediate or final output of the article ofmanufacture, further comprises: identifying one or more key influencersassociated with the manufacturing process; and adjusting one or morecontrol inputs associated with at least one of the one or more keyinfluencers.

Statement 5: The computer-implemented method of any of statements 1-4,further comprising: permitting control of the manufacturing process bythe one or more station controllers, if the predicted expected value forthe article of manufacture is in-specification.

Statement 6: The computer-implemented method of any of statements 1-5,further comprising: receiving a plurality of station values at the deeplearning controller, wherein the station values are associated with theone or more stations, and wherein predicting the expected value of thearticle of manufacture is further based on the station values.

Statement 7: The computer-implemented method of any of statements 1-6,wherein the plurality of control values comprises one or more of: speed,temperature, pressure, vacuum, rotation, current, voltage, resistance,or power.

Statement 8: A system comprising: one or more processors; and anon-transitory memory storing instructions that, when executed by theone or more processors, cause the one or more processors to performoperations comprising: receiving a plurality of control values from twoor more stations, at a deep learning controller, wherein the controlvalues are generated at the two or more stations deployed in amanufacturing process; predicting, by the deep learning controller, anexpected value for an intermediate or final output of an article ofmanufacture, based on the control values; determining, by the deeplearning controller, if the predicted expected value for the article ofmanufacture is in-specification; and generating, by the deep learningcontroller, one or more control inputs if the predicted expected valuefor the article of manufacture is not in-specification, wherein the oneor more control inputs are configured to cause an intermediate or finaloutput for the article of manufacture to be in-specification.

Statement 9: The system of statement 8, wherein the processors arefurther configured to perform operations comprising: outputting the oneor more control inputs to one or more station controllers, if aconfidence level associated with the one or more control inputs exceedsa predetermined threshold.

Statement 10: The system of any of statements 8-9, wherein outputtingthe one or more control inputs to the one or more station controllers,further comprises: providing a first control input to a first stationcontroller associated with a first station; and providing a secondcontrol input to a second station controller associated with a secondstation, and wherein the second station is downstream from the firststation in the manufacturing process.

Statement 11: The system of any of statements 8-10, wherein predictingthe expected value of the article of manufacture, further comprises:identifying one or more key influencers associated with themanufacturing process; and adjusting one or more control inputsassociated with at least one of the one or more key influencers.

Statement 12: The system of any of statements 8-11, further comprising:permitting control of the manufacturing process by the one or morestation controllers, if the predicted expected value for the article ofmanufacture is in-specification.

Statement 13: The system of any of statements 8-12, wherein theprocessors are further configured to perform operations comprising:receiving a plurality of station values at the deep learning controller,wherein the station values are associated with the one or more stations,and wherein predicting the expected value for the article of manufactureis further based on the station values.

Statement 14: The system of any of statements 8-13, wherein theplurality of control values comprises one or more of: speed,temperature, pressure, vacuum, rotation, current, voltage, resistance,or power.

Statement 15: A non-transitory computer-readable storage mediumcomprising instructions stored therein, which when executed by one ormore processors, cause the one or more processors to perform operationscomprising: receiving a plurality of control values from two or morestations, at a deep learning controller, wherein the control values aregenerated at the two or more stations deployed in a manufacturingprocess; predicting, by the deep learning controller, an expected valuefor the intermediate or final output of an article of manufacture, basedon the control values; determining, by the deep learning controller, ifthe predicted expected value for the article of manufacture isin-specification; and generating, by the deep learning controller, oneor more control inputs if the predicted expected value for the articleof manufacture is not in-specification, wherein the one or more controlinputs are configured to cause an intermediate or final output for thearticle of manufacture to be in-specification.

Statement 16: The non-transitory computer-readable storage medium ofstatement 15, further comprising instructions configured to cause theone or more processors to perform operations comprising: outputting theone or more control inputs to one or more station controllers, if aconfidence level associated with the one or more control inputs exceedsa predetermined threshold.

Statement 17: The non-transitory computer-readable storage medium of anyof statements 15-16, wherein outputting the one or more control inputsto the one or more station controllers, further comprises: providing afirst control input to a first station controller associated with afirst station; and providing a second control input to a second stationcontroller associated with a second station, and wherein the secondstation is downstream from the first station in the manufacturingprocess.

Statement 18: The non-transitory computer-readable storage medium of anyof statements 15-17, wherein predicting the expected value of thearticle of manufacture, further comprises: identifying one or more keyinfluencers associated with the manufacturing process;

and adjusting one or more control inputs associated with at least one ofthe one or more key influencers.

Statement 19: The non-transitory computer-readable storage medium of anyof statements 15-18, further comprising instructions configured to causethe one or more processors to perform operations comprising: permittingcontrol of the manufacturing process by the one or more stationcontrollers, if the predicted expected value for the article ofmanufacture is in-specification.

Statement 20: The non-transitory computer-readable storage medium of anyof statements 15-19, further comprising instructions configured to causethe one or more processors to perform operations comprising: receiving aplurality of station values at the deep learning controller, wherein thestation values are associated with the one or more stations, and whereinpredicting the expected value of the article of manufacture is furtherbased on the station values.

1. A computer-implemented method comprising: receiving a first set ofin-process inputs from a first processing station, at a deep learningcontroller, wherein the first set of in-process inputs are generated atthe first processing station deployed in a manufacturing process,wherein the first set of in-process inputs are attributes of the firstprocessing station of a plurality of stations in the manufacturingprocess; identifying, by the deep learning controller, a final qualitymetric of a plurality of final quality metrics for which to optimize themanufacturing process; predicting, by the deep learning controller, anexpected value of the final quality metric for an article of manufacturebased on the first set of in-process inputs; determining, by the deeplearning controller, that the expected value for the article ofmanufacture is not in-specification; determining, by the deep learningcontroller, a plurality of key influencers and parameters associatedwith the plurality of key influencers that impact the expected value forthe article of manufacture; and based on the determining, adjusting, bythe deep learning controller, downstream parameters of a downstreamprocessing station, the downstream parameters corresponding to theplurality of key influencers, wherein the adjusting causes the finalquality metric to be in-specification.
 2. The computer-implementedmethod of claim 1, further comprising: adjusting, by the deep learningcontroller, further downstream parameters of a further downstreamprocessing station in accordance with the plurality of key influencers.3. The computer-implemented method of claim 1, further comprising:generating, by the deep learning controller, one or more control inputs,wherein the deep learning controller adjusts the downstream parametersof the downstream processing station in accordance with the one or morecontrol inputs.
 4. The computer-implemented method of claim 3, whereinadjusting, by the deep learning controller, the downstream parameters ofthe downstream processing station comprises: identifying a control inputof the one or more control inputs that corresponds to a key influencerof the plurality of key influencers; and adjusting the downstreamparameters based on the control input corresponding to the keyinfluencer.
 5. The computer-implemented method of claim 1, wherein theplurality of key influencers is associated with a plurality ofparameters that affect characteristics of outputs from the firstprocessing station.
 6. The computer-implemented method of claim 1,wherein determining, by the deep learning controller, the plurality ofkey influencers and the parameters associated with the plurality of keyinfluencers that impact the expected value for the article ofmanufacture comprises: scoring a plurality of parameters in outputsgenerated by the first processing station; and identifying a subset ofthe plurality of parameters that exceed a threshold score.
 7. Thecomputer-implemented method of claim 1, wherein determining, by the deeplearning controller, the plurality of key influencers and the parametersassociated with the plurality of key influencers that impact theexpected value for the article of manufacture comprises: identifying aplurality of parameters in outputs generated by the first processingstation; and determining a subset of the plurality of parameters thatexceed a threshold level of influence on the outputs generated by thefirst processing station.
 8. A non-transitory computer readable mediumcomprising one or more sequences of instructions, which, when executedby a processor, causes a computing system to perform operationscomprising: receiving a first set of in-process inputs from a firstprocessing station, at a deep learning controller, wherein the first setof in-process inputs are generated at the first processing stationdeployed in a manufacturing process, wherein the first set of in-processinputs are attributes of the first processing station of a plurality ofstations in the manufacturing process; identifying, by the deep learningcontroller, a final quality metric of a plurality of final qualitymetrics for which to optimize the manufacturing process; predicting, bythe deep learning controller, an expected value of the final qualitymetric for an article of manufacture based on the first set ofin-process inputs; determining, by the deep learning controller, thatthe expected value for the article of manufacture is notin-specification; determining, by the deep learning controller, aplurality of key influencers and parameters associated with theplurality of key influencers that impact the expected value for thearticle of manufacture; and based on the determining, adjusting, by thedeep learning controller, downstream parameters of a downstreamprocessing station, the downstream parameters corresponding to theplurality of key influencers, wherein the adjusting causes the finalquality metric to be in-specification.
 9. The non-transitory computerreadable medium of claim 8, further comprising: adjusting, by the deeplearning controller, further downstream parameters of a furtherdownstream processing station in accordance with the plurality of keyinfluencers.
 10. The non-transitory computer readable medium of claim 8,further comprising: generating, by the deep learning controller, one ormore control inputs, wherein the deep learning controller adjusts thedownstream parameters of the downstream processing station in accordancewith the one or more control inputs.
 11. The non-transitory computerreadable medium of claim 10, wherein adjusting, by the deep learningcontroller, the downstream parameters of the downstream processingstation comprises: identifying a control input of the one or morecontrol inputs that corresponds to a key influencer of the plurality ofkey influencers; and adjusting the downstream parameters based on thecontrol input corresponding to the key influencer.
 12. Thenon-transitory computer readable medium of claim 8, wherein theplurality of key influencers is associated with a plurality ofparameters that affect characteristics of outputs from the firstprocessing station.
 13. The non-transitory computer readable medium ofclaim 8, wherein determining, by the deep learning controller, theplurality of key influencers and the parameters associated with theplurality of key influencers that impact the expected value for thearticle of manufacture comprises: scoring a plurality of parameters inoutputs generated by the first processing station; and identifying asubset of the plurality of parameters that exceed a threshold score. 14.The non-transitory computer readable medium of claim 8, whereindetermining, by the deep learning controller, the plurality of keyinfluencers and the parameters associated with the plurality of keyinfluencers that impact the expected value for the article ofmanufacture comprises: identifying a plurality of parameters in outputsgenerated by the first processing station; and determining a subset ofthe plurality of parameters that exceed a threshold level of influenceon the outputs generated by the first processing station.
 15. A systemcomprising: a processor; and a memory having programming instructionsstored thereon, which, when executed by the processor, causes the systemto perform operations comprising: receiving a first set of in-processinputs from a first processing station, at a deep learning controller,wherein the first set of in-process inputs are generated at the firstprocessing station deployed in a manufacturing process, wherein thefirst set of in-process inputs are attributes of the first processingstation of a plurality of stations in the manufacturing process;identifying, by the deep learning controller, a final quality metric ofa plurality of final quality metrics for which to optimize themanufacturing process; predicting, by the deep learning controller, anexpected value of the final quality metric for an article of manufacturebased on the first set of in-process inputs; determining, by the deeplearning controller, that the expected value for the article ofmanufacture is not in-specification; determining, by the deep learningcontroller, a plurality of key influencers and parameters associatedwith the plurality of key influencers that impact the expected value forthe article of manufacture; and based on the determining, adjusting, bythe deep learning controller, downstream parameters of a downstreamprocessing station, the downstream parameters corresponding to theplurality of key influencers, wherein the adjusting causes the finalquality metric to be in-specification.
 16. The system of claim 15,wherein the operations further comprise: generating, by the deeplearning controller, one or more control inputs, wherein the deeplearning controller adjusts the downstream parameters of the downstreamprocessing station in accordance with the one or more control inputs.17. The system of claim 16, wherein adjusting, by the deep learningcontroller, the downstream parameters of the downstream processingstation comprises: identifying a control input of the one or morecontrol inputs that corresponds to a key influencer of the plurality ofkey influencers; and adjusting the downstream parameters based on thecontrol input corresponding to the key influencer.
 18. The system ofclaim 15, wherein the plurality of key influencers is associated with aplurality of parameters that affect characteristics of outputs from thefirst processing station.
 19. The system of claim 15, whereindetermining, by the deep learning controller, the plurality of keyinfluencers and the parameters associated with the plurality of keyinfluencers that impact the expected value for the article ofmanufacture comprises: scoring a plurality of parameters in outputsgenerated by the first processing station; and identifying a subset ofthe plurality of parameters that exceed a threshold score.
 20. Thesystem of claim 15, wherein determining, by the deep learningcontroller, the plurality of key influencers and the parametersassociated with the plurality of key influencers that impact theexpected value for the article of manufacture comprises: identifying aplurality of parameters in outputs generated by the first processingstation; and determining a subset of the plurality of parameters thatexceed a threshold level of influence on the outputs generated by thefirst processing station.